A report discusses an algorithm for an onboard planning and execution technology to support the exploration and characterization of geological features by autonomous rovers. A rover that is capable of deciding which observations are more important relieves the engineering team from much of the burden of attempting to make accurate predictions of what the available rover resources will be in the future. Instead, the science and engineering teams can uplink a set of observation requests that may potentially oversubscribe resources and let the rover use observation priorities and its current assessment of available resources to make decisions about which observations to perform and when to perform them.
The algorithm gives the rover the ability to model spatial coverage quality based on data from different scientific instruments, to assess the impact of terrain on coverage quality, to incorporate user-defined priorities among subregions of the terrain to be covered, and to update coverage quality rankings of observations when terrain knowledge changes. When the rover is exploring large geographical features such as craters, channels, or boundaries between two different regions, an important factor in assessing the quality of a mission plan is how the set of chosen observations spatially cover the area of interest. The algorithm allows the rover to evaluate which observation to perform and to what extent the candidate observation will increase the spatial coverage of the plan.
This work was done by Daniel Gaines, Tara Estlin, and Caroline Chouinard of Caltech for NASA's Jet Propulsion Laboratory.
The software used in this innovation is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-44282.